As the world dissects the how and why of Donald Trump’s presidential victory, pollsters are already coming under severe criticism for getting it so wrong. But a tool we created that analysed emotions towards the two candidates on Twitter accurately predicted the result. Unlike other poll predictors that just take a snapshot in time, our tool used real-time data from Twitter which provides a more accurate picture of who people are likely to vote for.

We first developed the tool, called EMOTIVE, in 2013 to predict whether another riot could happen in London following those in 2011. It can analyse thousands of tweets a second to extract from each tweet a direct expression of one of eight basic emotions: anger, disgust, fear, happiness, sadness, surprise, shame and confusion.

We decided to use our tool to track emotions on Twitter around the world about the US presidential election for the three weeks leading up to the vote. During that period, our data showed that Trump continued to lead the race to the White House. Going by this tool, there were very few periods of time when Clinton was leading – contrary to what was being predicted by the polls.

The EMOTIVE system worked by looking at how much fluctuation there was in the number of tweets relating specific emotions towards either Trump and Clinton. The more tweets with reference to an emotion fluctuate – reflecting greater uncertainty towards a candidate – the fewer votes the model predicts a candidate is going to get. Crucially, it’s not the volume of tweets that matter, but the “choppiness” that make the difference. This means that the model does not concern itself with the direction of the emotion or put value on one emotion over another – because an emotion such as anger can be positive or negative.

This first graph shows how emotive tweets towards Trump fluctuated in that final 24-hour period. The y axis here measures what we call an emotional score. The wider the band of colour, the greater the number of emotive tweets related to that particular emotion, which means a greater outpouring of emotion towards Trump. But there was not a large amount of fluctuation overall in how much each emotion was appearing in tweets – meaning more voters were feeling more certain about their support for Trump.

Now look at the second graph, showing the same data in relation to Hillary Clinton over a slightly longer period. This shows greater fluctuation – more spikes and dips – and meant she was predicted to win fewer votes.

A good gauge of the public mood

When we analysed the raw tweets we saw an unprecedented campaign with a race all about personalities, dominated by attacks on Trump and Clinton.

There was very little mention of policy in the tweets we analysed, nor the difference it could make to the US and the world. This campaign saw the working class find their voice via digital communication channels.

This is not the first time the EMOTIVE tool has been used to predict an election result that the pollsters got wrong. In 2015, the system correctly predicted the outcome of the UK general election. And it has been put to other uses too. Another study looked at reactions to the November 2015 Paris terrorist attacks to potentially help survey those who would be at risk of post-traumatic stress.

Being able to monitor millions of digital sensors via Twitter and use them to map emotion provides a way to gauge the mood music of the average person on the street. And it is possible to get a real-life insight into what people really think about two candidates in an election.

In the case of the 2016 presidential elections, this is possibly why the polls were wrong throughout – often they were seen as the establishment trying to interfere in swaying opinion. While polls only provide a snapshot of that moment it time, systems such as EMOTIVE provide ongoing real-time analysis, which tends to provide a much more accurate picture of the landscape – and is proving good way at predicting which way elections may go.

Tom Jackson receives funding from EPSRC and DSTL under which the initial EMOTIVE system was developed.

Martin Sykora receives funding from EPSRC and DSTL under which the initial EMOTIVE system was developed.

It’s normal for conceding presidential candidates to give their speech in the middle of the night, as their guests are still at HQ and the final results are trickling in.

Hillary Clinton, however, decided not to do that after losing the 2016 election to Donald Trump. Instead her team arranged for a mid-morning speech on the day after the vote. It was held at a separate venue – at a hotel in New York – with seats for staff and supporters.

Speeches by losing candidates are interesting. The chosen tone, message, content and length tell us a lot about the state of democracy and the health of the candidate’s party. In fact, a CNN commentator claimed ahead of Clinton’s address that the concession speech is often more important for the health of democracy than the victory speech.

Clinton’s decision to make her speech in the morning means that she could say something more planned than if she had to cobble together a late-night reaction at a “let’s get it over with” event. It also means a better chance for news coverage, since a little time has passed since the upset of the result.

A later speech, according to commentators such as CNN, allowed Clinton to concede “on her own terms”. Crucially, it also meant not speaking in front of a set clearly designed for a victory speech.

Her role post result was to thank supporters but also to attempt to get some key messages across. Media have been briefed that Clinton’s speech would be a message to the American people about “the way forward”.

She stressed the need for participation over time. Democracy, she reminded her supporters, does not just mean casting a vote every four years, it’s a constant commitment. While she appealed to her audience to accept the result, the message was clear – continue to monitor Trump’s America.

“Our campaign was never about one person or one election,” she said. “It was about the country we love, about building a country that was hopeful, inclusive and big hearted.”

She was also keen to stress inclusion, shared values, and the “vision we hold for our country”, despite not making any direct references to concern that her opponent lacks these values.

There was a strong sense of passing on a torch to her supporters: “You represent the best of America,” she told the room, before promising that a woman president will happen one day.

I know we have still not shattered that highest and hardest glass ceiling but some day, someone will and hopefully sooner than we think right now.

Speaking to the alarm felt by many about the tone of the campaign, Clinton acknowledged the divisions in American society and stressed “the rule of law” and equality under the law. “We respect and cherish these values … and we must defend them,” she said.

Whether this was a goodbye, or a rallying call for a new movement, isn’t quite clear. But Clinton was certainly keen to focus on the future of her country rather than dwelling on her loss. She admitted the the result is painful and “will be for some time” and there was a moment when she looked ready to cry, but she pulled it back quickly.

So what now for Hillary Clinton? I’m not sure. But the delivery of a speech like this on a day which must be full of pain shows she is nothing if not a professional political operator.

Since records began in 1868, no clear favourite for the White House has lost, except in the case of the 1948 election, when 8 to 1 longshot Harry Truman defeated his Republican rival, Thomas Dewey.

We can now add 2016 to that list, thanks to Donald Trump, who has beaten 5 to 1 on favourite, Hillary Clinton, to take the presidency. In so doing, he also defied the polls, the experts and the wisdom of crowds.

I have been tracking various forecasting methodologies and prognosticators over the past few months, right up to election day, and can confirm that the rout of conventional wisdom was almost total.

Odds on

On the morning of the election, the best price available about Hillary Clinton was 7 to 2 on, equal to an implied win probability of about 78%. The spread betting markets made her a little over an 80% favourite, and gave her a head start over Trump of more than 80 electoral votes. The PredictIt prediction market assigned her a 79% chance of victory, and estimated her likely advantage as 323 electoral votes to 215 for Trump. Meanwhile, the Predictwise crowd wisdom platform assessed her chance of winning at a solid 89%, compared to 75% by the Hypermind prediction site.

The polling aggregation services fared no better. The RealClearPolitics and HuffPost Pollster polling averages gave Hillary Clinton a lead of between 3% and 6%. The FiveThirtyEight platform, which removes bias from polls based on their previous performance, gave her a popular vote lead on the day of 3.6% and an electoral vote advantage of 67 over Trump. Her chance of winning was assessed as 71.9% based on this polling.

Perhaps the biggest failure of the night, however, was Sam Wang’s Princeton Election Consortium, which gave Clinton more than a 99% chance of victory. Still, it must be said that his topline figures (an electoral college advantage of 307 to 231 for Trump, and 2.5% in the popular vote) were less far off than a number of the other forecasting methodologies.

The New York Times Upshot elections model, which bases its estimates on state and national polls, gave Clinton a 84% chance of victory, which they helpfully compared to the chance of an NFL kicker making a 38-yard field goal. About 16% of the time they miss. That was the same chance as Hillary Clinton losing, they suggested.

Talking heads

Expert opinion was also woefully off. One of the most high-profile providers of expert political opinion is the Sabato Crystal Ball, run by Larry Sabato of the University of Virginia’s Center for Politics. This service has a very good track record. Yet, in line with the polls and the markets, the Crystal Ball got it badly wrong this time. Its final prediction was a win for Hillary Clinton by 322 electoral votes to 216.

It is the PollyVote election forecasting service which provides perhaps the most broad-based expert opinion survey, however, calling on its own panel of political experts to periodically update its forecast of the likely two-way vote share of the main candidates. The final expert panel survey, conducted on the eve of the election, put Clinton 4.4% up over Trump (52.2% to 47.8%).

In attempting to estimate the final vote share tallies of the candidates, PollyVote provides not just the estimates of experts, but also evidence gathered from a range of other methodologies, including prediction markets, poll aggregators, econometric models, citizen forecasts and index models. The idea is that aggregating and combining the wisdom of each and taking an average should provide a better estimate than any in isolation. It is a methodology which has served well over the past three election cycles.

This time the methodology broke down as badly as any of the main forecasting methodologies in isolation. Taking them in turn, the prediction market indicator (based on the trading in the Iowa electronic markets) gave Hillary Clinton a lead of 54.6% to 45.4%. Using data from RealClearPolitics and HuffPost Pollster to construct its poll aggregation metric, it gave the lead to Clinton by 52% to 48%.

PollyVote also highlights the various econometric forecasting models available, which typically use variables such as growth, unemployment, incumbency, and so on, to provide an aggregated estimate. That estimate was, this time, quite successful, giving Clinton the advantage in the popular vote of 50.2% to 49.8%. Winning the popular vote is, however, not the same thing as winning the electoral college, as Democrats in particular have learned in recent years.

The final two methodologies used to make up the PollyVote forecast are index models, which use information about the candidates, and citizen forecasts, which ask people whom they expect to win. The index models this time gave Clinton the edge over trump by 53.5% to 46.5%, and the citizen forecasts by 52.2% to 47.8%. Combining all these methodologies together produced an estimated advantage for Clinton over Trump of 52.5% to 47.5%.

The bottom line, therefore, is that most of the tried and tested forecasting methodologies failed this time. Election 2016 truly demonstrated, on a grand scale, the madness of crowds, polls and experts.

Leighton Vaughan Williams does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond the academic appointment above.

"Scientists say they may have found a way to protect babies in the womb from the harmful effects of Zika," BBC News reports.

Researchers have had success using antibody therapy to treat mice when they were still in their mothers' womb.

There is evidence that Zika virus, which has become widespread in South America recently, can damage the development of babies in the womb. One of the most striking birth defects associated with Zika is babies being born with abnormally small heads and brains (microcephaly).

The hope is that by treating babies in the womb it may be possible to prevent, or at least reduce the extent of, birth defects.

The study involved isolating strains of antibodies (infection-fighting proteins) from the blood of people who'd recovered from Zika. Scientists picked the antibodies that were most active against several strains of the virus. They then tested their effect on pregnant mice infected with Zika.

The mouse foetuses were much more likely to survive if their mothers had been given antibodies, and there was less evidence of damage to the foetus or placenta.

Results in mice cannot tell us whether the treatment will be safe or effective in humans. So the researchers say the treatment should next be tested on monkeys, as their pregnancies and reactions to Zika virus are more similar to humans.

The need for effective Zika treatments is pressing as a study from earlier this summer estimated the current epidemic would last for at least three more years.

Where did the story come from?

The study was carried out by researchers from Vanderbilt University Medical Center in Nashville and Washington University School of Medicine in the US.

It was funded by the US National Institutes of Health and grants from the charitable institutions Burroughs Wellcome Fund and the March of Dimes.

What kind of research was this?

Research in mice is a common early step when scientists are developing a treatment, but it doesn't tell us whether the treatment will be safe or effective in humans.

What did the research involve?

Researchers analysed blood from three people who'd had Zika, and isolated antibodies that seemed to bind to the Zika virus and inhibit its spread. They tested the most promising antibody as a treatment for mice infected with Zika virus, and also on pregnant mice infected with the virus.

They compared results for those given the antibody treatment and those given an inactive treatment.

Because mice have natural resistance to Zika virus, the researchers had to give them a treatment that suppressed their immune system and made them more vulnerable to the infection.

After treatment, researchers checked to see how long the mice survived, how many of the mouse pregnancies survived, and how much virus was found in the placenta or the mice brains.

They also tested giving the treatment before the mice were infected with Zika, on the same day, or five days after infection.

What were the basic results?

Mice treated with antibodies on the day after infection all survived for at least 20 days, while only 40% of untreated mice survived Zika infection for 20 days.

Later treatment was less successful, but mice treated five days after infection were still much more likely to survive.

Almost all mouse pregnancies survived up to 13 days where the mother had been treated with antibodies a day before being infected with Zika virus, while most pregnancies of untreated mice did not survive Zika virus infection.

When the researchers looked at tissues from the mice at the end of the study, they found much higher concentrations of Zika virus in the head of the mouse foetus and the foetal placenta, in untreated mice, compared to those treated with antibodies.

Levels of the virus were also higher in the brains and blood of the mice mothers who didn't have antibody treatment.

How did the researchers interpret the results?

The researchers say they've shown that antibody therapy, either before or after exposure to Zika virus, "reduced infection in mothers, and in placental and fetal tissues." Importantly, they say that "the extent to which these observations in mice translate to humans remains unclear", and recommend further animal studies in monkeys.

They say that if these results were positive, antibody treatment could be developed as a way of treating Zika infection during pregnancy.

Conclusion

For most people, Zika virus infection causes a mild flu-like illness. But it can cause serious damage to unborn children, if their mothers catch the virus while they are pregnant.

At present, there's no treatment that can help protect these babies against the effect of the virus, so news that a treatment may be on the way is welcome.

However, this research is in the very early stages. Mice and humans react very differently to Zika virus, and there are important differences in the structures of mouse and human bodies during pregnancy.

This means we don't know whether this treatment would work in the same way, or if it would even be safe for humans. Much more work is needed before this is a viable human treatment.

For now, the best thing you can do is to try to avoid becoming infected in the first place – especially if you're pregnant.

Pregnant women are being advised to postpone non-essential travel to areas with active Zika virus transmission. If you travel to an affected area, you can reduce your risk of catching the virus by using insect repellent and wearing loose clothing that covers your arms and legs.

The governor of Bungoma county said he had ordered a market survey of the price of stainless steel wheelbarrows after the county bought 10 for more than KSh1 million. Not to worry, governor Ken Lusaka, Africa Check did it for you.

The Labour Party will today outline plans to fund 125,000 new homes over the course of the next Parliament using money saved in the Help to Buy ISAs announced by the Chancellor in last month’s budget. These 125,000 over five years will be part of a wider scheme to build 200,000 each year by 2020....

Last week UKIP set out their plan for exit from the European Union, outlining two options: “We repeal the European Communities Act 1972 and leave immediately. We activate Article 50 of the Lisbon Treaty and notify the European Council that the UK has decided to leave the EU in two years’ time.” —UKIP manifesto The […]

"Average adult catches virus just once every five years," the Daily Mail reports.

A study has estimated that influenza infections become less frequent with age and occur every five years from the age of 30.

The study analysed blood samples from volunteers in southern China, looking at antibody levels against nine different strains of influenza that circulated from 1968 to 2009. Using complex mathematical models, researchers estimated the frequency of influenza infections and how immunity changes over a lifetime as people encounter different strains of the virus.

The "twice in a decade" figure may sound surprisingly low, but it is only an estimated average for influenza A. It does not include infections with strains of influenza B or C. Also, the estimate is based on a small sample of just 150 people with an age range from seven to 64. Results may well differ in other countries.

It is important not to be complacent as flu can be dangerous. Precise figures are hard to come by, as flu is often a factor in increasing the risk of fatal complications, rather than a cause of death. A 2013 study estimated that flu was implicated in around 13,000 elderly deaths in England and Wales during the flu season from 2008 to 2009.

Where did the story come from?

The study was carried out by researchers from the London School of Hygiene and Tropical Medicine, Imperial College London and University of Liverpool in the UK; Johns Hopkins Bloomberg School of Public Health in the US; the University of Hong Kong; and Shantou University and Guangzhou No 12 Hospital, in China.

It was funded by the Medical Research Council, the National Institute for Health Research and the Wellcome Trust in the UK; and Fogarty International Centre, the Department of Homeland Security and the National Institute for General Medical Sciences in the US.

This was a highly complex scientific paper (a sample quote – "Hence the titre μ was scaled by a factor s1(X, j) = (1 + τ1)|X"), so unsurprisingly, the media focused on the simple message that according to this study, flu is far less common than many people think. The Daily Mail also reported that "man flu" may be a myth, with no evidence that men are more likely than women to be "struck down" by the bug. The study itself does not look at rates of infection for each sex.

What kind of research was this?

In this study, scientists aimed to look at how our immunity to flu – specifically to influenza A strain (H3N2) – changes over a lifetime as we encounter different strains of the virus. It is important to understand this they say, because how the immune response develops influences the emergence of new strains of the virus, the size and severity of flu epidemics and the effectiveness of vaccination programmes. They say that factors that shape the human immune response are poorly understood, since individual infections and the development of immunity over a lifetime are rarely observed directly.

The immune system responds to flu viruses by producing antibodies that specifically target proteins on the virus surface. These proteins can change as the virus evolves, but we keep antibodies in the blood that have a memory for strains we have encountered before.

What did the research involve?

There were two parts to this study.

Scientists used data from a survey in southern China that examined people’s antibody levels against nine different strains of influenza A (H3N2) from 1968 to 2009. Participants were selected from five different locations, with 20 households randomly selected from each location. Samples of blood were taken and tested for the presence of antibodies against different strains of flu.

To determine the effect of a lifetime of influenza infections on immunity, scientists developed a mathematical model capturing the specific strains with which an individual has been infected and the corresponding antibody response. They examined whether this was affected by factors such as:

"cross-reactivity", increased immune response to a new strain due to previous antibody response to a different strain

What were the basic results?

Their model found that "antigenic seniority" and the reduction in cross-reactivity over time were important components of the immune response.

They estimate that while children on average get flu every other year, infections become less frequent as people get older. From the age of 30 onwards, they estimate that flu infections tend to occur at the rate of about two every 10 years.

How did the researchers interpret the results?

The researchers say that the strains encountered early in life and the order in which individuals were infected with the flu virus influence their immune response, which in turn could shape the evolution of the flu virus. These findings, they argue, could also help us better understand future susceptibility to new strains and develop future vaccination programmes.

Conclusion

This complex scientific study looked at which factors might influence the immune response to flu over someone’s lifetime and also produced an estimate of how frequently people in different age groups are affected by flu. The details are of interest mainly to other scientists involved in studying the flu virus, how it may evolve and the best way to protect ourselves against it.

When considering the results, it is important to note that these are estimates. They are based on blood samples from 150 people. This means there would have been a limited number of people in each age group, which spanned age seven to 64. In addition, the participants were selected from 20 households in each of five study locations in southern China. People living together are more likely to infect each other with the virus, and so the results may be different among other population groups.

The estimates are also based on nine strains that were originally recorded in 1968, 1975, 1979, 1989, 1995, 2002, 2003, 2005 and 2008. It does not cover other strains, influenza B or C, or whether the immune response was due to previous vaccination or infection.

Additionally, the researchers had to make a number of assumptions, which need to be taken into account when considering the results:

They estimated the number of times people had been infected by each strain by assuming each subsequent infection with the same strain of virus would boost the immune response.

They considered that the immune response to a new strain would not be as high as to previous strains, with the first-ever infection creating the biggest immune response.

It's claimed the new higher minimum wage could cost the care sector £1 billion. Some of this cost could be incurred anyway without the new policy, and the figure rests on a lot of uncertainty. Other estimates even suggest higher costs.

The Liberal Democrats have pledged to “Extend free school meals to all children in primary education as resources allow and following a full evaluation of free meals for infants”. We don’t know yet what impact the policy is having. Universal Infant Free School Meals were introduced for all infants in reception, year 1 and year 2 […]